Is there a way to save a new column based on several conditions in python?-CodePudding

My problem is that I want to create a new column based on two conditions: if type == condition, and name == depression. It is necessary that two conditions like these are satisfied at the same time.

Here is the demonstration data:

#for demonstration
import pandas as pd

example = {
"ID": [1, 2,3, 4, 5],
"type": ["condition", "temperature", "condition", "condition", "status"],
"name":  ["depression", "high", "fatigue", "depression", "positive"],
}

#load into df:
example = pd.DataFrame(example)

print(example)

The result I expect to get look like this:

#for demonstration
import pandas as pd

result = {
"ID": [1, 2,3, 4, 5],
"type": ["condition", "temperature", "condition", "condition", "status"],
"name":  ["depression", "high", "fatigue", "depression", "positive"],
"depression":["yes", "no","no","yes", "no"]
}

#load into df:
result = pd.DataFrame(result)

print(result)

The most closest to the solution what I've tried was this:

example["depression"]= example[example.type=="condition"].name == "depression"

and as output it gives

But this is not what I want, because it classify others as NAN, and gives True or False as values. What I want is in the result df above.

Could you please help me to find out how to deal with it? Thanks in advance!

CodePudding user response：

You can use np.where to do that:

example["depression"] = np.where(example.type.eq("condition") & example.name.eq("depression"), "yes", "no")

print(example)
-------------------------------------------------------
    ID  type        name        depression
0   1   condition   depression  yes
1   2   temperature high        no
2   3   condition   fatigue     yes
3   4   condition   depression  yes
4   5   status      positive    no
-------------------------------------------------------

It returns you the second argument ('yes') for all True conditions and the third argument ('no') else. The conditions are specified in the first argument.